Fast registration of 3D fetal ultrasound images using learned corresponding salient points

TY - CHAP

T1 - Fast registration of 3D fetal ultrasound images using learned corresponding salient points

AU - Gomez Herrero, Alberto

AU - Bhatia, Kanwal

AU - Tharin, Sarjana

AU - Housden, James

AU - Toussaint, Nicolas

AU - Schnabel, Julia

PY - 2017

Y1 - 2017

N2 - We propose a fast feature-based rigid registration framework with a novel feature saliency detection technique. The method works by automatically classifying candidate image points as salient or non-salient using a support vector machine trained on points which have previously driven successful registrations. Resulting candidate salient points are used for symmetric matching based on local descriptor similarity and followed by RANSAC outlier rejection to obtain the final transform. The proposed registration framework was applied to 3D real-time fetal ultrasound images, thus covering the entire fetal anatomy for extended FoV imaging. Our method was applied to data from 5 patients, and compared to a conventional saliency point detection method (SIFT) in terms of computational time, quality of the point detection and registration accuracy. Our method achieved similar accuracy and similar saliency detection quality in < 5% the detection time, showing promising capabilities towards real-time whole-body fetal ultrasound imaging.

AB - We propose a fast feature-based rigid registration framework with a novel feature saliency detection technique. The method works by automatically classifying candidate image points as salient or non-salient using a support vector machine trained on points which have previously driven successful registrations. Resulting candidate salient points are used for symmetric matching based on local descriptor similarity and followed by RANSAC outlier rejection to obtain the final transform. The proposed registration framework was applied to 3D real-time fetal ultrasound images, thus covering the entire fetal anatomy for extended FoV imaging. Our method was applied to data from 5 patients, and compared to a conventional saliency point detection method (SIFT) in terms of computational time, quality of the point detection and registration accuracy. Our method achieved similar accuracy and similar saliency detection quality in < 5% the detection time, showing promising capabilities towards real-time whole-body fetal ultrasound imaging.

UR - http://www.scopus.com/inward/record.url?scp=85029796745&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-67561-9_4

DO - 10.1007/978-3-319-67561-9_4

M3 - Other chapter contribution

AN - SCOPUS:85029796745

SN - 9783319675602

VL - 10554 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 33

EP - 41

BT - Fetal, Infant and Ophthalmic Medical Image Analysis - International Workshop, FIFI 2017 and 4th International Workshop, OMIA 2017 Held in Conjunction with MICCAI 2017, Proceedings

PB - Springer Verlag

T2 - International Workshop on Fetal and Infant Image Analysis, FIFI 2017 and 4th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2017 held in Conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017

Y2 - 14 September 2017 through 14 September 2017

ER -